Journal of Biopharmaceutical Statistics最新文献_第6页

Conditional and marginal hazard ratios: accounting for unmeasured confounding in interpretation and estimation. 条件和边际风险比：解释和估计中未测量的混杂因素。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-09-18 DOI: 10.1080/10543406.2026.2728050

Pablo Martínez-Camblor, Todd A MacKenzie, Jesse A Columbo, A James O'Malley

{"title":"Conditional and marginal hazard ratios: accounting for unmeasured confounding in interpretation and estimation.","authors":"Pablo Martínez-Camblor, Todd A MacKenzie, Jesse A Columbo, A James O'Malley","doi":"10.1080/10543406.2026.2728050","DOIUrl":"https://doi.org/10.1080/10543406.2026.2728050","url":null,"abstract":"Conditional proportional hazard Cox regression models are routinely used in the analysis of time-to-event outcomes, with the association between one variable of interest and the outcome commonly reported as a hazard ratio (HR). However, the interpretation of HRs is strongly dependent on the underlying assumptions of the model, which can make causal interpretation prone to inaccuracy. The non-collapsibility of the model contributes heavily to such confusion, as it disqualifies the same model from holding conditionally and marginally, even when the study design is a randomized clinical trial (RCT). With the goal of facilitating causal interpretation of the results, alternative Cox-type models, such as marginal structural models, have been proposed. These models are collapsible, and therefore their parameters can be identified in the absence of unmeasured confounders. In this document, we provide a probabilistic interpretation of both conditional and marginal HRs; highlight the differences in the underlying models, and point out the subtleties in their interpretation. We discuss some limitations related to the real-world estimation of HRs. In particular, we explore the limitations of propensity‑score techniques when estimating conditional hazard ratios in the presence of unmeasured confounders. A synthetic dataset is used to illustrate the problem. In a practical application, we emulate a study in which the target was to compare the effectiveness of two procedures for treating carotid artery stenosis patients: transfemoral carotid artery stenting (TF-CAS), and a more recent procedure that was approved for use in 2015, transcarotid artery revascularization (TCAR).","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian variable selection on small sample trial data via adaptive posterior-informed shrinkage prior. 贝叶斯变量选择小样本试验数据通过自适应后验通知收缩先验。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-09-18 DOI: 10.1080/10543406.2026.2729801

Lingxuan Kong, Yumin Zhang, Chenkun Wang, Yaoyuan Vincent Tan

{"title":"Bayesian variable selection on small sample trial data via adaptive posterior-informed shrinkage prior.","authors":"Lingxuan Kong, Yumin Zhang, Chenkun Wang, Yaoyuan Vincent Tan","doi":"10.1080/10543406.2026.2729801","DOIUrl":"https://doi.org/10.1080/10543406.2026.2729801","url":null,"abstract":"Identifying variables associated with clinical endpoints is a central objective in clinical trials. With the rapid expansion of cell and gene therapies (CGTs) and therapeutics for ultra-rare diseases, there is an urgent demand for statistical methods capable of detecting meaningful associations under severe sample-size constraints. Motivated by data-borrowing strategies for historical controls, we propose the Adaptive Posterior-Informed Shrinkage Prior (APSP), a Bayesian framework that adaptively leverages external information to improve variable-selection efficiency while preserving robustness across diverse scenarios. APSP extends existing Bayesian borrowing methods by incorporating data-driven adaptive information selection, mixture shrinkage informative priors, and empirical-null-based decision rules, thereby enhancing variable-selection performance in small-sample settings. Extensive simulations demonstrated that APSP consistently matches or exceeds the efficiency of traditional and widely used Bayesian data-borrowing and variable-selection approaches. Notably, it exhibits strong robustness against possible discrepancies between internal and external data across a broad range of simulation scenarios. Finally, we applied APSP to the Clinical Islet Transplantation (CIT) Consortium, identifying variables associated with peak C-peptide at Day 75 in study CIT-06 by borrowing information from study CIT-07.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An integrated methodological roadmap for real-world biomarker studies: advancing oncology precision medicine through robust methodologies and machine learning integration. 现实世界生物标志物研究的综合方法学路线图：通过强大的方法学和机器学习集成推进肿瘤精准医学。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-11 DOI: 10.1080/10543406.2026.2703576

Dai Feng, Amber Lind, Weili He

{"title":"An integrated methodological roadmap for real-world biomarker studies: advancing oncology precision medicine through robust methodologies and machine learning integration.","authors":"Dai Feng, Amber Lind, Weili He","doi":"10.1080/10543406.2026.2703576","DOIUrl":"https://doi.org/10.1080/10543406.2026.2703576","url":null,"abstract":"The selection of biomarker-specific patient populations is essential in targeted cancer therapies to enhance precision and efficacy. To ensure a successful launch, it is vital to promote awareness and adoption of biomarker testing at diagnosis, tailor implementation strategies to accommodate local variations, and ensure testing is accessible and reimbursed. An integrated evidence generation plan should address critical questions, including the prevalence of biomarker expression and agreement between local and central labs, across different platforms, antibodies and pathologists. Furthermore, understanding prognostic effects and associations with other biomarkers is of considerable interest. Real-world studies (RWS) play a pivotal role in addressing these questions but are inherently challenged by confounding factors, biases (e.g. immortal time bias), missing data, agreement assessment complexities, and low biomarker expression prevalence. This manuscript provides an integrated methodological roadmap for designing and analyzing RWS. We address key challenges by integrating robust statistical methodologies with advanced machine learning (ML) methods. Core methods discussed include the use of time-dependent Cox models to mitigate immortal time bias, inverse probability of biomarker weighting to adjust for confounding, and ML-based tree ensemble approaches to model complex relationships between covariates and outcomes and handle missing data. By systematically applying this integrated roadmap, we demonstrate how to enhance the validity and robustness of RW biomarker research. This approach overcomes common analytical pitfalls, enabling more reliable evidence generation for clinical decision-making. Ultimately, this roadmap helps drive precision oncology forward by ensuring that biomarker-driven therapeutic strategies are based on sound, high-quality RW evidence.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.3,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Randomization-based covariance analysis for hypothesis testing of treatment comparisons based on restricted mean survival time with categorized time-to-event data. 基于限制平均生存时间和分类事件时间数据的治疗比较的随机化协方差分析的假设检验。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-05 DOI: 10.1080/10543406.2026.2699854

Taylor J Krajewski, Gary G Koch

{"title":"Randomization-based covariance analysis for hypothesis testing of treatment comparisons based on restricted mean survival time with categorized time-to-event data.","authors":"Taylor J Krajewski, Gary G Koch","doi":"10.1080/10543406.2026.2699854","DOIUrl":"https://doi.org/10.1080/10543406.2026.2699854","url":null,"abstract":"This paper introduces randomization-based analysis of covariance (RB-ANCOVA) for hypothesis testing of restricted mean survival time (RMST) differences between two randomized treatments in trials with categorized time-to-event data. RMST treatment differences over a prespecified time period are clinically meaningful and avoid assumptions like proportional hazards. The proposed method tests the strong null hypothesis that each participant's time-to-event would remain unchanged regardless of treatment assignment. Covariate adjustment is achieved by constraining baseline covariate means to be equal across arms, which reduces variance. The follow-up period is partitioned into mutually exclusive intervals, and RMST is approximated as the area under the survival curve. Under the strong null hypothesis, the joint asymptotic covariance structure of RMST and covariate means is known and used to construct a chi-squared test statistic for the covariate-adjusted RMST difference. The method accommodates stratified trial designs and supports hypothesis testing over single or several time intervals, enabling testing within internal intervals. The difference in RMSTs over an interval can be divided by its length to yield an average survival rate difference. Additionally, the approach allows the computation of essentially exact p-values via re-randomization. We illustrate the method with data from a randomized, placebo-controlled trial evaluating a test treatment for amyotrophic lateral sclerosis. This method provides a hypothesis testing approach for RMST treatment differences that leverages RB-ANCOVA to adjust for their correlations with baseline covariate differences. Under the strong null hypothesis, it enables hypothesis testing with clear control of Type I error and reduced variance without relying on model-based assumptions.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction. 修正。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-02-05 DOI: 10.1080/10543406.2025.2461926

引用次数: 0

Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis. 贝叶斯动态功率先验借用，用于增强控制臂的生存分析。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-06-26 DOI: 10.1080/10543406.2025.2519153

Jixian Wang, Sanhita Sengupta, Ram Tiwari

{"title":"Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis.","authors":"Jixian Wang, Sanhita Sengupta, Ram Tiwari","doi":"10.1080/10543406.2025.2519153","DOIUrl":"10.1080/10543406.2025.2519153","url":null,"abstract":"The use of real-world data, containing data from historical clinical studies, to construct an external control arm or to augment a small internal control arm in a randomized control trial can lead to significant improvements in the efficiency of the trial, but it may also introduce bias. To mitigate the risk of potential bias arising from the heterogeneity between the external control and the internal control arms, Bayesian dynamic borrowing, which determines the amount of borrowing by similarity between the two data sources, using power prior approaches and covariate adjustment has been introduced. For binary and continuous outcomes, an approach integrating propensity score for covariate adjustment and Bayesian dynamic borrowing using power prior has been proposed. Here, we extend this approach to survival analysis with the hazard ratio as the estimand. We propose a novel approach for estimating the amount of borrowing using the empirical Bayes method based on the log-hazard ratio between external and internal controls. For inference, the approach uses Bayesian bootstrap in combination with the empirical Bayes method, covariate adjustment, and multiple imputation, taking into account all uncertainty. The performance of our approach is examined by a simulation study. As an illustration, we apply the approach to dynamic borrowing of Flatiron real-world data for CheckMate-057 study for advanced non-squamous non-small cell lung cancer. For this application, we apply multiple imputation for missing covariates and propose a computationally efficient algorithm for computing the total variance of the log hazard ratio estimate. The proposed method can be applied to other endpoints in oncology as well as to other disease areas.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"973-994"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weighted log-rank tests for censored data under Wei's urn design: saddlepoint approximation and confidence intervals. 魏氏瓮设计下删失数据的加权对数秩检验：鞍点近似和置信区间。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2023-03-03 DOI: 10.1080/10543406.2023.2183508

Abd El-Raheem M Abd El-Raheem, Ehab F Abd-Elfattah

{"title":"Weighted log-rank tests for censored data under Wei's urn design: saddlepoint approximation and confidence intervals.","authors":"Abd El-Raheem M Abd El-Raheem, Ehab F Abd-Elfattah","doi":"10.1080/10543406.2023.2183508","DOIUrl":"10.1080/10543406.2023.2183508","url":null,"abstract":"The randomization designs in clinical trials provide probabilistic basis for the statistical inference of the permutation tests. One of the widely used designs to avoid the problems of imbalance and selection bias for one of the treatments is Wei's urn design. In this article, the saddlepoint approximation is suggested to approximate the p-values of the weighted log-rank class of two-sample tests under Wei's urn design. To show the accuracy of the proposed method and to clarify its procedure, two sets of real data are analyzed, and a simulation study is conducted using different sample sizes and three different life time distributions. Through the illustrative examples and simulation study, a comparison is made between the proposed method and the traditional method, which is the normal approximation method. All of these procedures confirmed that the proposed method is more accurate and efficient than the normal approximation method in approximating the exact p-value of the considered class of tests. As a result, the nominal <math><mn>95</mn><mi>%</mi></math> confidence intervals for the treatment effect are determined.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"732-741"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10830683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BOP2-FE: Bayesian optimal phase II design with futility and efficacy-stopping boundaries. BOP2-FE：具有无效和有效性停止边界的贝叶斯最优II期设计。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-09-17 DOI: 10.1080/10543406.2025.2558142

Xinling Xu, Atsuki Hashimoto, Belay B Yimer, Kentaro Takeda

引用次数: 0

Recovery of overall survival information from treatment switching in oncology trials using multiple imputation. 在肿瘤试验中使用多重输入从治疗转换中恢复总体生存信息。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-11-21 DOI: 10.1080/10543406.2025.2571224

Jianbo Xu

{"title":"Recovery of overall survival information from treatment switching in oncology trials using multiple imputation.","authors":"Jianbo Xu","doi":"10.1080/10543406.2025.2571224","DOIUrl":"10.1080/10543406.2025.2571224","url":null,"abstract":"In oncology trials, patients in both the control and experimental arms can receive different subsequent anti-cancer therapies (SATs) after discontinuing their randomized study drugs, a phenomenon commonly referred to as treatment switching. SATs may have the potential to extend overall survival (OS) in patients treated with the control and experimental drugs. Without recovering the information from the SATs, the statistical power of the clinical trials could be drastically reduced, thus making it difficult or impossible to meet the efficacy objective. This article presents a novel statistical method for imputing the post-switching survival time multiple times to derive the point estimate of the true hazard ratio (HR) of OS between the experimental and control drugs and the associated 95% confidence interval (CI). The proposed method provides an effective solution for recovering lost information in the OS caused by SATs. It also offers an efficient way to evaluate the true causal treatment effect, potentially increasing the statistical power. Additionally, this method can be used for patients with a crossover from a placebo to an experimental treatment in placebo-controlled trials. Simulation studies demonstrated that the proposed method performed well and reliably, and applications to oncology trials using the simulated data are provided.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1118-1143"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The performance of odds ratio estimation under different scenarios in Bayesian meta-analysis: A simulation study. 贝叶斯元分析中不同情况下优势比估计的性能：模拟研究。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-11-08 DOI: 10.1080/10543406.2025.2575941

Esin Avci

{"title":"The performance of odds ratio estimation under different scenarios in Bayesian meta-analysis: A simulation study.","authors":"Esin Avci","doi":"10.1080/10543406.2025.2575941","DOIUrl":"10.1080/10543406.2025.2575941","url":null,"abstract":"This study presents a comprehensive evaluation of Bayesian meta-analysis methods for estimating odds ratios (ORs), with a focus on the impact of heterogeneity and prior distribution choices under varying conditions. Recognizing the limitations of frequentist approaches, especially in small-sample or rare-event scenarios, we implemented a Bayesian framework utilizing four different priors for heterogeneity: half-normal, exponential, half-Cauchy, and inverse-gamma. Simulation studies were conducted across 1,152 scenarios, varying the number of studies, event rarity, randomization ratios, and baseline risks. Results indicate that prior specification and study size substantially influence estimation accuracy, particularly for rare events. To further explore these interactions, CHAID (Chi-square Automatic Interaction Detection) analysis, which effectively identified key factors affecting model performance, is implemented. CHAID revealed that the number of studies included in the meta-analysis (NSMA) is the most significant determinant of estimation reliability, while other variables such as event type and randomization ratio exert notable influence under specific conditions. CHAID also facilitated the categorization of OR estimation quality and heterogeneity levels, offering a powerful visual and interpretive aid. Overall, this study underscores the importance of prior selection in Bayesian meta-analysis and highlights CHAID analysis as a valuable complementary tool for uncovering complex interactions and enhancing result interpretability.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1184-1210"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0